深度学习实验四:卷积神经网络编程
本次实验练习使用torch.nn中的类设计一个卷积神经网络进行MNIST手写体数字图像分类。
name = 'x'#填写你的姓名
sid = 'B02014152'#填写你的学号
print('姓名:%s, 学号:%s'%(name, sid))
姓名:x, 学号:B02014152
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
import matplotlib.pyplot as plt
1. 设计CNN类
从torch.nn.Module派生一个子类CNN，表示一个卷积神经网络;
请合理设计网络各个层，尝试使用不同的结构，比如skip connection, batch normalization,group convolution等。
你的网络模型中至少有2个卷积层、2个聚合层。
#在下面添加代码，实现卷积神经网络,用于识别MNIST手写体数字
# conv1 = nn.Conv2d(in_channels = 3, out_channels = 16, kernel_size = 3, padding = 0) # Valid Conv
# conv2 = nn.Conv2d(in_channels = 3, out_channels = 16, kernel_size = 3, padding = 1) # Same Conv
# conv3 = nn.Conv2d(in_channels = 3, out_channels = 16, kernel_size = 3, padding = 2) # Full Conv
class CNN(torch.nn.Module):
def __init__(self):
super().__init__()
self.convd1 = nn.Conv2d(in_channels = 1, out_channels = 10, kernel_size = 3, padding = 0)
self.pool1 = torch.nn.MaxPool2d(kernel_size= 2, stride= 2)
self.convd2 = nn.Conv2d(in_channels = 10, out_channels = 20, kernel_size = 3, padding = 0)
self.pool2 = torch.nn.MaxPool2d(kernel_size= 2, stride= 2)
self.fc = torch.nn.Linear(500, 10)
def forward(self, x):
x.cuda()
batch_size = x.size(0)
z1 = self.pool1(self.convd1(x))
a1 = F.relu(z1)
z2 = self.pool2(self.convd2(a1))
a2 = F.relu(z2)
a2 = a2.view(batch_size, -1)
a2 = self.fc(a2)
# a2 = F.softmax(a2, dim=0)
return a2
#测试MLP类
X = torch.rand((32,1,28,28),dtype = torch.float32)
net = CNN()
Y = net(X)
print(Y)
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
Cell In [482], line 4
2 X = torch.rand((32,1,28,28),dtype = torch.float32)
3 net = CNN()
----> 4 Y = net(X)
5 print(Y)
File ~\AppData\Roaming\Python\Python38\site-packages\torch\nn\modules\module.py:727, in Module._call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
730 self._forward_hooks.values()):
731 hook_result = hook(self, input, result)
Cell In [481], line 19, in CNN.forward(self, x)
17 batch_size = x.size(0)
18 x = nn.BatchNorm2d(num_features=1)
---> 19 z1 = self.pool1(self.convd1(x))
20 a1 = F.relu(z1)
21 z2 = self.pool2(self.convd2(a1))
File ~\AppData\Roaming\Python\Python38\site-packages\torch\nn\modules\module.py:727, in Module._call_impl(self, *input, **kwargs)
725 result = self._slow_forward(*input, **kwargs)
726 else:
--> 727 result = self.forward(*input, **kwargs)
728 for hook in itertools.chain(
729 _global_forward_hooks.values(),
730 self._forward_hooks.values()):
731 hook_result = hook(self, input, result)
File ~\AppData\Roaming\Python\Python38\site-packages\torch\nn\modules\conv.py:423, in Conv2d.forward(self, input)
422 def forward(self, input: Tensor) -> Tensor:
--> 423 return self._conv_forward(input, self.weight)
File ~\AppData\Roaming\Python\Python38\site-packages\torch\nn\modules\conv.py:419, in Conv2d._conv_forward(self, input, weight)
415 if self.padding_mode != 'zeros':
416 return F.conv2d(F.pad(input, self._reversed_padding_repeated_twice, mode=self.padding_mode),
417 weight, self.bias, self.stride,
418 _pair(0), self.dilation, self.groups)
--> 419 return F.conv2d(input, weight, self.bias, self.stride,
420 self.padding, self.dilation, self.groups)
TypeError: conv2d(): argument 'input' (position 1) must be Tensor, not BatchNorm2d
Y.shape
torch.Size([32, 10])
#了解MLP中的参数
for name,param in net.named_parameters():
print(name,':',param.size())
convd1.weight : torch.Size([10, 1, 3, 3])
convd1.bias : torch.Size([10])
convd2.weight : torch.Size([20, 10, 3, 3])
convd2.bias : torch.Size([20])
fc.weight : torch.Size([10, 500])
fc.bias : torch.Size([10])
#输出模型
print(net)
CNN(
(convd1): Conv2d(1, 10, kernel_size=(3, 3), stride=(1, 1))
(pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(convd2): Conv2d(10, 20, kernel_size=(3, 3), stride=(1, 1))
(pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(fc): Linear(in_features=500, out_features=10, bias=True)
)
#用torchsummary输出模型结构
# !pip install -i https://pypi.tuna.tsinghua.edu.cn/simple torchsummary
from torchsummary import summary
summary(net.cuda(), input_size = (1,28,28))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Conv2d-1 [-1, 10, 26, 26] 100
MaxPool2d-2 [-1, 10, 13, 13] 0
Conv2d-3 [-1, 20, 11, 11] 1,820
MaxPool2d-4 [-1, 20, 5, 5] 0
Linear-5 [-1, 10] 5,010
================================================================
Total params: 6,930
Trainable params: 6,930
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.09
Params size (MB): 0.03
Estimated Total Size (MB): 0.12
----------------------------------------------------------------
2.训练模型
2.1 第一步，对数据做预处理
MNIST图像的像素取值范围是[0,1]，先把值域改变为[-1,1]. 在PyTorch中，可以使用torchvision.transforms.Normalize类处理。
from torchvision import datasets,transforms
#构造一个变换，将像素值范围变换到[-1,1]
normalizer = transforms.Normalize((0.5,), (0.5,))#一行代码
#定义一个变换序列transform，包含两个变换：第一个将PIL图像转换为张量，第二个是normalizer
transform = transforms.Compose([transforms.ToTensor(), normalizer]) #一行代码
2.2 第二步，构造训练集，加入预处理
data_path = '../data/'
mnist_train = datasets.MNIST(data_path,download=False,train = True,transform = transform)
mnist_test = datasets.MNIST(data_path,download=False,train = False,transform = transform)
2.3 第三步，构造加载器
batch_size = 32 #可以自己定义batch_size大小
train_loader = torch.utils.data.DataLoader(mnist_train, batch_size = batch_size, shuffle = True)
test_loader = torch.utils.data.DataLoader(mnist_test, batch_size = batch_size, shuffle = False)
#从加载器里获取一批样本，并输出样本张量的形状
imgs,labels = iter(train_loader).next()#一行样本
imgs.shape
torch.Size([32, 1, 28, 28])
labels.shape
torch.Size([32])
2.4 第四步，训练模型
注意：训练卷积神经网络时，网络的输入是四维张量，尺寸为 N × C × H × W N\times C \times H \times W N×C×H×W，分别表示张量
def Train(model, loader, epochs, lr = 0.1):
epsilon = 1e-6
#将model置于train模式
#一行代码
net.train()
#定义合适的优化器
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)#一行代码
#定义损失函数
loss = F.cross_entropy#一行代码
#请在下面完成训练代码
#请在迭代过程中每100次迭代，输出一次损失
loss0 = 0
for epoch in range(epochs):
for it,(imgs, labels) in enumerate(loader):
#1. zero_grads
#请用一行代码实现
optimizer.zero_grad()
imgs, labels = imgs.cuda(), labels.cuda()
#2. F.P.前向传播
#请用一行代码实现
logits = model(imgs)
#3. 计算损失
loss1 = loss(logits, labels)#请用一行代码实现
if(abs(loss1.item() - loss0)<epsilon):
break
loss0 = loss1.item()
if it%100==0:
print('epoch %d, iter %d, loss = %f\n'%(epoch,it,loss1.item()))
#4. 后向传播
#请用一行代码实现
loss1.backward()
#5. 梯度下降
#请用一行代码实现.
optimizer.step()
return model
#训练模型
model = CNN()
model = model.cuda()
model = Train(model, test_loader, 10)
epoch 0, iter 0, loss = 2.317181
epoch 0, iter 100, loss = 1.612653
epoch 0, iter 200, loss = 0.475313
epoch 0, iter 300, loss = 0.267556
epoch 1, iter 0, loss = 0.272252
epoch 1, iter 100, loss = 0.387812
epoch 1, iter 200, loss = 0.248591
epoch 1, iter 300, loss = 0.130585
epoch 2, iter 0, loss = 0.101499
epoch 2, iter 100, loss = 0.289020
epoch 2, iter 200, loss = 0.127526
epoch 2, iter 300, loss = 0.072299
epoch 3, iter 0, loss = 0.069821
epoch 3, iter 100, loss = 0.237272
epoch 3, iter 200, loss = 0.073975
epoch 3, iter 300, loss = 0.052888
epoch 4, iter 0, loss = 0.055265
epoch 4, iter 100, loss = 0.210844
epoch 4, iter 200, loss = 0.046827
epoch 4, iter 300, loss = 0.046028
epoch 5, iter 0, loss = 0.044145
epoch 5, iter 100, loss = 0.192648
epoch 5, iter 200, loss = 0.031938
epoch 5, iter 300, loss = 0.042316
epoch 6, iter 0, loss = 0.036941
epoch 6, iter 100, loss = 0.179299
epoch 6, iter 200, loss = 0.023668
epoch 6, iter 300, loss = 0.039010
epoch 7, iter 0, loss = 0.032049
epoch 7, iter 100, loss = 0.168726
epoch 7, iter 200, loss = 0.018773
epoch 7, iter 300, loss = 0.036361
epoch 8, iter 0, loss = 0.027803
epoch 8, iter 100, loss = 0.158944
epoch 8, iter 200, loss = 0.015419
epoch 8, iter 300, loss = 0.034879
epoch 9, iter 0, loss = 0.024135
epoch 9, iter 100, loss = 0.149009
epoch 9, iter 200, loss = 0.012855
epoch 9, iter 300, loss = 0.033128
2.5 第五步，测试模型
#编写模型测试过程
def Evaluate(model, loader):
model.eval()
correct = 0
counts = 0
for imgs, labels in loader:
imgs, labels = imgs.cuda(), labels.cuda()
logits = model(imgs)
yhat = logits.argmax(dim = 1)
correct = correct + (yhat==labels).sum().item()
counts = counts + imgs.size(0)
accuracy = correct / counts
return accuracy
acc = Evaluate(model,test_loader)
print('Accuracy = %f'%(acc))
Accuracy = 0.974000
imgs,labels = next(iter(test_loader))
imgs, labels = imgs.cuda(), labels.cuda()
logits = model(imgs)
imgs, labels = imgs.cpu(), labels.cpu()
yhat = logits.argmax(dim = 1)
plt.figure(figsize = (16,10))
for i in range(imgs.size(0)):
plt.subplot(4,8,i+1)
plt.imshow(imgs[i].squeeze()/2+0.5,cmap = 'gray')
plt.axis('off')
plt.title('GT=%d, Pred = %d'%(labels[i],yhat[i]))
plt.show()
转载：https://blog.csdn.net/code_bro/article/details/128402650
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